Estimating the cost of data-mining services

ABSTRACT

The cost of data-mining is estimated where data-mining services are delivered via a distributed computing system environment. System requirements are estimated for a particular data-mining task for an input data set having specified properties. Estimating system requirements includes applying a partial learning tool to operate on sample data from the input data set.

BACKGROUND

The present invention relates generally to the field of data processingand more particularly to database and file access cost estimates.

Information about the expected cost of complex operations is of keyimportance for software as a service offerings. Customers are oftenbilled dynamically based on actual resource consumption. This is truefor tasks that usually consume large amounts of computer processing(CPU), disk I/O, and main memory, such as data-mining and big dataanalytics. Solutions exist for estimating the cost of resources neededfor simple queries on large data bases, using an execution plan.

SUMMARY

According to an aspect of the present invention, there is a method,computer program product, and/or system that performs the followingsteps (not necessarily in the following order): (i) receiving a set oftask parameters, the set of task parameters defining a target data setand a data-mining task; (ii) receiving a set of control values, the setof control values describing the data-mining task; (iii) receiving a setof data descriptors, the set of data descriptors describing the targetdata set; and (iv) estimating a set of computational resources requiredto perform the data-mining task over a distributed computing systembased at least in part on the set of task parameters, the set of controlvalues, the set of data descriptors, and an availability of thedistributed computing system. At least the estimating step is performedby computer software running on computer hardware.

According to an aspect of the present invention, there is a method,computer program product, and/or system that performs the followingsteps (not necessarily in the following order): (i) receiving a dataset; (ii) receiving a set of control values; (iii) receiving a set ofdata-mining task parameters; and (iv) estimating a set of computationalresources required to perform a data-mining task. The set of controlvalues describes the data-mining task. The set of data-mining taskparameters defines the data set and the data-mining task. The set ofcomputational resources is based at least in part on the data set, theset of control values, and the set of data-mining task parameters. Atleast the estimating step is performed by computer software running oncomputer hardware.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a cloud computing node according to an embodiment of thepresent invention;

FIG. 2 depicts a cloud computing environment according to an embodimentof the present invention;

FIG. 3 depicts abstraction model layers according to an embodiment ofthe present invention;

FIG. 4 is a flowchart showing a first embodiment method performed, atleast in part, by the first embodiment system;

FIG. 5 is a block diagram view of a machine logic (for example,software) portion of the first embodiment system;

FIG. 6 depicts a data-mining environment according to a secondembodiment of the present invention;

FIG. 7 depicts a data-mining environment according to a third embodimentof the present invention;

FIG. 8 depicts a data-mining environment according to a fourthembodiment of the present invention; and

FIG. 9 depicts a data-mining environment according to a fifth embodimentof the present invention.

DETAILED DESCRIPTION

The cost of data-mining is estimated where data-mining services aredelivered via a distributed computing system environment. Systemrequirements are estimated for a particular data-mining task for aninput data set having specified properties. Estimating systemrequirements includes applying a partial learning tool to operate onsample data from the input data set.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based email). Theconsumer does not manage or control the underlying cloud infrastructureincluding network, servers, operating systems, storage, or evenindividual application capabilities, with the possible exception oflimited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, processing units 16, a system memory 28, and a bus 18 that couplesvarious system components including system memory 28 to processing units16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,system memory 28 may include at least one program product having a set(e.g., at least one) of program modules that are configured to carry outthe functions of embodiments of the invention.

Program/utility 40, having set of program modules 42, may be stored insystem memory 28 by way of example, and not limitation, as well as anoperating system, one or more application programs, other programmodules, and program data. Each of the operating system, one or moreapplication programs, other program modules, and program data or somecombination thereof, may include an implementation of a networkingenvironment. Set of program modules 42 generally carry out the functionsand/or methodologies of embodiments of the invention as describedherein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes (e.g., cloud computing node 10) with which localcomputing devices used by cloud consumers, such as, for example,personal digital assistant (PDA) or cellular telephone 54A, desktopcomputer 54B, laptop computer 54C, and/or automobile computer system 54Nmay communicate. Cloud computing nodes may communicate with one another.They may be grouped (not shown) physically or virtually, in one or morenetworks, such as Private, Community, Public, or Hybrid clouds asdescribed hereinabove, or a combination thereof. This allows cloudcomputing environment 50 to offer infrastructure, platforms and/orsoftware as services for which a cloud consumer does not need tomaintain resources on a local computing device. It is understood thatthe types of computing devices 54A-N shown in FIG. 2 are intended to beillustrative only and that cloud computing node 10 and cloud computingenvironment 50 can communicate with any type of computerized device overany type of network and/or network addressable connection (e.g., using aweb browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 2) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided.

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include mainframes; RISC(Reduced Instruction Set Computer) architecture based servers; storagedevices; networks and networking components. In some embodimentssoftware components include network application server software.

Virtualization layer 62 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients.

In one example, management layer 64 may provide the functions describedbelow. Resource provisioning provides dynamic procurement of computingresources and other resources that are utilized to perform tasks withinthe cloud computing environment. Metering and Pricing provide costtracking as resources are utilized within the cloud computingenvironment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal provides access to the cloud computing environment forconsumers and system administrators. Service level management providescloud computing resource allocation and management such that requiredservice levels are met. Service Level Agreement (SLA) planning andfulfillment provide pre-arrangement for, and procurement of, cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA.

Workloads layer 66 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation; software development and lifecycle management; virtualclassroom education delivery; data analytics processing; transactionprocessing; and functionality according to the present invention (seefunction block 66 a) as will be discussed in detail, below, in thefollowing sub-sections of this detailed description section.

Some embodiments of the present invention relate to acomputer-implemented method for estimating computational resources forrunning a data-mining task over a distributed computing system, themethod comprising: (i) receiving a data set, based on which thedata-mining task is performed and/or receiving a set of data descriptorsdescribing or bounding features of the data set relevant for estimatingcomputational resources for running the data-mining task; (ii) receivinga set of control values for the data-mining task; (iii) receiving a setof task parameters, the set of task parameters defining the data-miningtask to be performed on the data set; (iv) estimating a set ofcomputational resources for performing the data-mining task over thedistributed computing system based on the received data set or thereceived set of data descriptors, the set of control values, and the setof task parameters.

Some embodiments of the present invention include providing a set ofcost data based on a value range of a set of control variables. In someembodiments of the present invention, a set of cost data includesmultiple cost values associated with a set of control variables. In someembodiments of the present invention, a set of cost data is derived fora time period or a range of accuracy. In some embodiments of the presentinvention, a set of cost data is provided to a user over a graphicaluser interface. In some embodiments of the present invention, a set ofcost data is displayed as a function of a set of control variables. Insome embodiments of the present invention, a set of cost data isprovided to a user as a graph. In some embodiments of the presentinvention, a set of cost data is displayed as a dependency on a set ofcontrol values.

In some embodiments of the present invention, a set of feedbackinformation is received. In some embodiments of the present invention, aset of feedback information includes information regarding a set ofprior data-mining task cost estimations. In some embodiments of thepresent invention, a set of feedback information includes a set ofactual costs for a set of prior data-mining task cost estimations. Insome embodiments of the present invention, a cost estimate is calculatedbased, at least in part, on a set of actual costs for a set of priordata-mining task cost estimate and on a set of prior data-mining taskcost estimates. In some embodiments of the present invention,calculation of a cost estimate includes an adaptive machine learningalgorithm. In some embodiments of the present invention, an adaptivemachine learning algorithm improve the cost estimation. In someembodiments of the present invention, an adaptive machine learningalgorithm improves a set of cost estimates to reduce the differencebetween a set of costs estimates and a set of actual costs.

Some embodiments of the present invention include one or more of thefollowing features: (i) calculating a set of process flows forprocessing a data-mining task; (ii) estimating a computational effortrequired to process a data-mining task for each process flow in a set ofprocess flows; (iii) estimating a cost to process a data-mining task ona distributed computing system for each process flow in a set of processflows; and/or (iv) choosing a process flow in a set of process flowshaving a lowest cost to process a data-mining task. In some embodimentsof the present invention, a process flow in the set of process flows isselected according to the lowest cost while complying with a set ofother constraints. In some embodiments of the present invention,selecting a process flow of a set of process flows based on costimproves a set of future cost estimates.

FIG. 4 shows flowchart 450 depicting a method for estimating thecomputational effort of a data-mining task, including the computationalresources necessary for running a data-mining task based on a blockdiagram, according to the present invention. FIG. 5 shows program 500,which performs at least some of the method steps of flowchart 450. Thismethod and associated software will now be discussed, over the course ofthe following paragraphs, with extensive reference to FIG. 4 (for themethod step blocks) and FIG. 5 (for the software blocks). In thisexample computer system/server 12 determines the resources required tocomplete a data-mining task.

Processing begins at step S455, where receive data set module (“mod”)505 receives a data set on which a data-mining task is to be performed.In some embodiments of the present invention, the data set is a set ofdata descriptors. In some embodiments of the present invention, the datais a matrix A with m rows and N columns, Expression (1):A∈R ^(m×n)  (1)

In some embodiments of the present invention, receive data set mod 505receives a data set and a set of data descriptors. In some embodimentsof the present invention, the set of data descriptors describes a set ofcharacteristics of the data in the data set. In some embodiments of thepresent invention, the set of data descriptors indicates whether thedata set has a certain data property. In some embodiments of the presentinvention, the set of data descriptors describes and/or bounds a set offeatures of the data set that are relevant for estimating computationalresources required for running a data-mining task. In some embodimentsof the present invention, the set of data descriptors describes thecomputational resources necessary for running a data-mining task basedon a block diagram.

In some embodiments of the present invention, the set of datadescriptors describe a set of key features of the data set. In someembodiments of the present invention, the set of data descriptors bounda set of key features of the data set. In some embodiments of thepresent invention, the set of key features of the data set are relevantfor estimating the computational resources required for the data-miningtask. In some embodiments of the present invention, separation of theset of data descriptors from the data set is complex. In someembodiments the set of data descriptors result from a set ofcomputations on the data set.

Processing proceeds to step S460, where receive control values mod 510receives a set of control values associated with the data-mining task.In some embodiments of the present invention, the set of control valuesinfluences the amount of computational resources required for processinga data-mining task. In some embodiments of the present invention,control values mod 510 is influenced by the accuracy of the estimationand the point of time until the estimation must be completed. In someembodiments of the present inventions, receive control values mod 510receive a set of control values from a user or an application. In someembodiments of the present invention, receive control values mod 510receive the set of control values from an estimation system.

Processing proceeds to step S465, where receive task parameters mod 515receives a set of task parameters. In some embodiments of the presentinvention, the set of task parameters defines a set of characteristicsof the data-mining task to be performed on the data set. In someembodiments of the present invention, the set of task parametersincludes an algorithm classifier. In some embodiments of the presentinvention, the algorithm classifier specifies the algorithm to be usedto perform the data-mining task. In some embodiments of the presentinvention, the algorithm classifier directly denotes an algorithm to beused. In some embodiments of the present invention, the algorithmclassifier includes a set of metadata. In some embodiments of thepresent invention, the set of metadata included in the algorithmclassifier indicates a certain data-mining algorithm. In someembodiments of the present invention, the algorithm classifier indicatesthat the data-mining task is a regression task. In some embodiments ofthe present invention, the algorithm classifier indicates that thedata-mining task is a classification task. In some embodiments of thepresent invention, the algorithm classifier indicates that thedata-mining task is a clustering task.

In some embodiments of the present invention, a regression task (or aregression model) involves a data-mining function to predict acontinuous value based on a set of information included in a data set.In some embodiments of the present invention, a regression model is usedto predict the value of a house based on a data set including factorsabout the house (e.g., location, size, nearby property values). In someembodiments of the present invention a classification task (or aclassification model) involves a data-mining function to assign a set ofitems to a set of classes or to a set of categories. In some embodimentsof the present invention, a classification model is used to predict atarget class for each datum in a data set. In some embodiments of thepresent invention, a classification model is used to classify a set ofemails as “spam” or “not spam.” In some embodiments of the presentinvention a clustering task (or a clustering model) involves adata-mining function to identify a similar set of data, based on a setof characteristics. In some embodiments of the present invention, aclustering model is used to identify a set of high-quality clusters suchthat the inter-cluster similarity is low. In some embodiments of thepresent invention, a clustering model is used to identify a high-qualitycluster such that the intra-cluster similarity is high. In someembodiments of the present invention, regression models, classificationmodels, and clustering models are employed for a variety of data-miningtasks.

In some embodiments of the present invention, the data-mining functionis a supervised data-mining process. In some embodiments of the presentinvention, the data-mining function is an unsupervised data-miningprocess. In some embodiments of the present invention, an unsuperviseddata-mining process includes machine learning. In some embodiments ofthe present invention, a regression model is a supervised data-miningprocess. In some embodiments of the present invention, a classificationmodel is a supervised data-mining process. In some embodiments of thepresent invention, a clustering model is an unsupervised data-miningprocess. In some embodiments of the present invention, superviseddata-mining includes a set of training data. In some embodiments of thepresent invention, computer system/server 12 must correlate the data setin a similar manner to the set of training data, a “similarity.” In someembodiments of the present invention, a regression model creates acontinuous interval. In some embodiments of the present invention, aclassification model creates a set of discrete intervals. In someembodiments of the present invention, a set of training data isrepresented by a matrix A, see Expression (1), above. In someembodiments of the present invention, a row in the matrix A correspondsto an element in a vector y:y∈R ^(m)  (2)

In some embodiments of the present invention, a column in the matrix Ais called a “feature.” In some embodiments of the present invention, arow in the matrix A is called an “observation.” In some embodiments ofthe present invention, a “similarity” is expressed by a loss function.In some embodiments of the present invention, a loss function measureshow similar items are. In some embodiments of the present invention,loss function may be one of:

_(SL)(x,A _(j:) ,y ^((j)))):=½(y ^((j)) A _(j:) x)²  (3)

_(LL)(x,A _(j:) ,y ^((j))):=log(1+e ^(−y) ^((j)) ^(A) ^(J) ^(x))  (4)

_(HL)(x,A _(j:) ,y ^((j))):=½max{0,1−y ^((j)A) _(j:) x} ²  (5)

In some embodiments of the present invention, Expression (3) representsa “square loss.” In some embodiments of the present invention,Expression (4) represents a “logistic loss.” In some embodiments of thepresent invention, Expression (5) represents a “hinge square loss.” Insome embodiments of the present invention, A_(j:) represents the jth rowof the matrix A.

In some embodiments of the present invention, a regularization functioncaptures a set of preferences among similar items. For example, humanshave a preference for simple-to-explain things, which can be captured bythe number coefficients required. In some embodiments of the presentinvention, a column in the matrix A corresponds to an element in avector x:x∈R ^(N)  (6)

In some embodiments of the present invention, a composite function isused to find a vector x:

$\begin{matrix}{{\min\limits_{x \in R^{N}}{F(x)}}:={{y{x}_{1}} + {\sum\limits_{j = 1}^{m}{\mathcal{L}\left( {x,A_{j:},y^{(j)}} \right)}}}} & (7)\end{matrix}$

In some embodiments of the present invention,

(x, A_(j:), y^((j))) represents one of the loss Expressions (2), (3), or(4). In some embodiments of the present invention, ∥x∥₁ represents thenumber of non-zero elements of the vector x. In some embodiments of thepresent invention, a convex combination is employed with a coefficientγ∈R.

In some embodiments of the present invention, the set of datadescriptors received in step S455 vary with the type of data-miningfunction employed by computer system-server 12. In some embodiments ofthe present invention, a set of data descriptors for superviseddata-mining functions includes, but is not limited to: (i) a number, n,of features in (the dimension of) the data set; (ii) a number, m, ofdescribed items in the data set; (iii) for a block-structured data set,a number of blocks in the data set; (iv) a degree of partialseparability of a smooth convex term of Expression (7),

(e.g. k-cut in a corresponding hypergraph); (v) a power of the largesteigenvalue, L=σ_(max) ²(A); (vi) a condition number,σ_(max)(A)/σ_(min)(A); and/or (vii) a number of non-zero values in acolumn or block i of matrix A, ∥A_(:i)∥₀. In some embodiments of thepresent invention, the condition number is based on a set of eigenvaluesof the matrix A. In some embodiments of the present invention,σ_(max)(A) is the largest eigenvalue of the matrix A. In someembodiments of the present invention, σ_(min)(A) is the smallesteigenvalue of the matrix A. In some embodiments of the presentinvention, the condition number relates to the number of iterationsperformed. In some embodiments of the present invention, ∥A_(:i)∥₀provides a bound on the smoothness of the regularization function. Insome embodiments of the present invention, ∥A_(:i)∥₀. Provides a boundon the partial separability of the regularization function. In someembodiments of the present invention, ∥_(:i)∥₀ replaces a set of datadescriptors that incur more computing resources.

In some embodiments of the present invention, unsupervised data-mining(or unsupervised machine learning) partitions a set of elements throughclustering. In some embodiments of the present invention, a clusteringalgorithm operates on matrix A, represented by Expression (1). In someembodiments of the present invention, the rows of matrix A correspond toa set of instances (or a set of observations). In some embodiments ofthe present invention, the set of instances include a set of N features.In some embodiments of the present invention, a set of clusteringalgorithms operate on a weighted graph G=(V, W). In some embodiments ofthe present invention, matrix W (also matrix W(i,j)) represents a set ofweights connecting vertices i and j. In some embodiments of the presentinvention, matrix W is an m×m matrix. In some embodiments of the presentinvention, matrix W is represented by the corresponding graph Laplacianmatrix L. In some embodiments of the present invention, a clusteringalgorithm requires a notion of similarity between data instances. Insome embodiments of the present invention, a clustering algorithmrequires an objective function that determines the structure of theoptimal data partitioning. In some embodiments of the present invention,a clustering algorithm is selected from the group:

$\begin{matrix}{{CA}:={\arg{\min\limits_{({C_{1},C_{2},\ldots,C_{k}})}\left( {\sum\limits_{i = 1}^{k}{\sum\limits_{x_{j} \in C_{i}}{{x_{j} - \mu_{k}}}}} \right)}}} & (8) \\{{CA}:={\arg{\min\limits_{({C_{1},C_{2},\ldots,C_{k}})}\left( {\sum\limits_{i = 1}^{k}{\sum\limits_{{j \in C_{i}},{m \in {\overset{\_}{C}}_{i}}}\frac{W_{j\; m}}{{vol}\left( C_{i} \right)}}} \right)}}} & (9)\end{matrix}$

In some embodiments of the present invention, Expression (8) is ak-means clustering algorithm. In some embodiments of the presentinvention, Expression (9) is a normalized cut clustering algorithm.

In some embodiments of the present invention, Expression (8) identifiesthe optimal k clusters, C₁ through C_(k), such that a set of Euclideandistances between the instances of x_(j) (a row in matrix A) and therespective cluster centroid, is minimized. In some embodiments of thepresent invention, Expression (9) works with graph inputs to identify aclustering of vertices on the graph to minimize the edge weights to be“cut” while normalizing for cluster size.

In some embodiments of the present invention, a clustering algorithmuses a set of data descriptors to describe or bound key features of thedata set. In some embodiments of the present invention, key features ofthe data set are relevant to estimate the computational resourcesrequired for a data-mining task. In some embodiments of the presentinvention, a set of data descriptors includes, but is not limited to:(i) the number, n, of features (the dimension) of the training set; (ii)the number, m, of described items in the training set; (iii) in a blockstructure, the number of blocks; (iv) the k eigenvector conditionnumber, such that σ_(k)(A)/σ_(k+1) (A) is used to determine a number ofiterations performed; (v) the number of non-zero values in any column orblock i of the matrix A, ∥A_(:i)∥₀, is used to determine the computationcost of the spectral decomposition of matrix A.

Processing terminates at step S470, where estimate mod 520 estimates thecomputational effort required for the data-mining task based on the dataset received in step S455, the set of control values received in step460, and the set of task parameters received at step S465. In someembodiments of the present invention, estimate mod 520 bases itsestimates on the received data set. In some embodiments of the presentinvention, estimate mod 520 separately identifies the received data setand the set of data descriptors. In some embodiments of the presentinvention, estimate mod 520 bases its estimates on the set of controlvalues. In some embodiments of the present invention, estimate mod 520bases its estimates on the set of task parameters. In some embodimentsof the present invention, estimate mod 520 estimates the computationaleffort required to perform a data-mining task over a distributedcomputing system. In some embodiments of the present invention, thedistributed computing system is a node in a cloud computing environment.In some embodiments of the present invention, the distributed computingsystem includes multiple nodes in a cloud computing environment. In someembodiments of the present invention, estimate mod 520 estimates theprocessing power required for the data-mining task. In some embodimentsof the present invention, estimate mod 520 estimates the memory requiredfor the data-mining task. In some embodiments of the present invention,estimate mod 520 estimates the I/O tasks required for the data-miningtask. In some embodiments of the present invention, estimate mod 520estimates the network tasks required for the data-mining task. In someembodiments of the present invention, estimate mod 520 considers anumber of iterations through the data set required to complete thedata-mining task. In some embodiments of the present invention, thenumber of iterations through the data set is a function of a desiredaccuracy level of the estimation. In some embodiments of the presentinvention, the number of iterations through the data set (data passes)is a function of the number of computing entities used for processingthe estimation algorithm. In some embodiments of the present invention,estimate mod 520 performs the estimate by considering the number ofarithmetic operations required in a data pass. In some embodiments ofthe present invention, an arithmetic operation includes a broadcastoperation. In some embodiments of the present invention, a broadcastoperation transmits a set of bytes. In some embodiments of the presentinvention, a broadcast operation transmits a set of bytes in apoint-to-point communication. In some embodiments of the presentinvention, a broadcast operation includes a point-to-pointcommunication.

In some embodiments of the present invention, the estimate produced byestimate mod 520 depends on whether the task parameter indicates aregression task, a classification task, or a clustering task. In someembodiments of the present invention, the estimate produced by estimatemod 520 depends on a loss-function. In some embodiments of the presentinvention, the loss function is correlated with the task parameter.

In some embodiments of the present invention, estimate mod 520 defines adata-mining task using a job definer. In some embodiments of the presentinvention a job definer composes a job for processing the data-miningtask. In some embodiments of the present invention, a job definerincludes a set of logic for selecting a suitable method to process adata-mining task, based, at least in part, on a data set, a set ofcontrol values, and a set of task parameters. In some embodiments of thepresent invention, estimate mod 520 estimates a computational complexityof a data-mining task, based in part on a job definition. In someembodiments of the present invention, a computational complexity of adata-mining task is based in part on a set of cost descriptors. In someembodiments of the present invention, a set of cost descriptors includesinformation about costs associated with a set of component parts of adata-mining task.

In some embodiments of the present invention, a set of cost descriptorsincludes, but is not limited to: (i) a number of iterations as afunction of a number of machines employed; (ii) a number of data passesacross a set of coordinates as a function of a number of machinesemployed; (iii) an acceptable error probability for an estimate; (iv) anumber of arithmetic operations for a data pass as a function of anumber of machines employed; (v) a number of broadcast operations perdata pass; (vi) a number of bytes transmitted per broadcast operation;(vii) a number of point-to-point communication operations per data pass;and/or (viii) a number of bytes transmitted per point-to-pointcommunication.

Some embodiments of the present invention provide cost descriptors for aset of supervised data-mining tasks. In some embodiments of the presentinvention, a supervised data-mining task includes a regression taskand/or a classification task. In some embodiments of the presentinvention, a cost per arithmetic operation performed is based, at leastin part on a number of iterations and a complexity of a set ofiterations. In some embodiments of the present invention, a cost percommunication performed is based, at least in part on a number ofiterations and a complexity of a set of iterations. In some embodimentsof the present invention, a number of iterations is based, at least inpart, on a number of data passes and a number of coordinates. In someembodiments of the present invention, a block structure is used and anumber of blocks of coordinates is used in place of a number ofcoordinates.

In some embodiments of the present invention, a probabilistic upperbound for a number of iterations is determined. In some embodiments ofthe present invention, an iteration counter is calculated. In someembodiments of the present invention, k is an iteration counter. In someembodiments of the present invention, x₀ is an initial point. In someembodiments of the present invention, 0<ρ<1 is a target confidence. Insome embodiments of the present invention,

_(L) ²(x₀)=max_(x){max_(x*∈X*)∥x−x*∥_(L) ²|F(x)≤F(x₀)}. In someembodiments of the present invention, ∈>0. In some embodiments of thepresent invention, F*=max_(x)F(x). In some embodiments of the presentinvention, a set of constants includes, but is not limited to: a, β, σ,s, ∥h∥_(L) ², and/or L.

In some embodiments of the present invention, an iteration counter isdetermined as:

k ≥ 2 ⁢ ⁢ max ⁢ { β ⁢ L 2 ⁢ ( x 0 ) , F ⁡ ( x 0 ) - F * } αϵ ⁢ ( 1 + log ⁢ 1 ρ) + 2 ( 10 )

In some embodiments of the present invention, ∈<F(x₀)−F*. Alternatively,an iteration counter is determined as:

$\begin{matrix}{k \geq {\frac{2\beta{}_{L}^{2}\left( x_{0} \right)}{\alpha\epsilon}\log\frac{{F\left( x_{0} \right)} - F^{*}}{\epsilon\;\rho}}} & (11)\end{matrix}$

In some embodiments of the present invention, ∈<max{β

_(L) ²(x₀), F(x₀)−F*}.

In some embodiments of the present invention, a probabilistic functionexists such that for a point, x_(k), a non-increasing check is appliedto a convex function, F. In some embodiments of the present invention aset of parameters ∈, ρ, and L are crucial to determining a number ofiterations. In some embodiments of the present invention, aprobabilistic function is represented as:Prob(F(x _(k))−F*≤∈)≥1−ρ  (12)

In some embodiments of the present invention, a single iteration isrequired. In some embodiments of the present invention, a singleiteration requires a set of operations, O(∥A_(:i)∥₀), to determine acost of a data-mining task. In some embodiments of the presentinvention, ∥A_(:i)∥₀ is the number of non-zero values in column (orblock) i of matrix A. In some embodiments of the present invention, thenumber of operations, O, for the data-mining task is reduced based, atleast in part, on an eigenvalue of matrix A. In some embodiments of thepresent invention, the number of operations is selected from a methodincluding, but not limited to: (i) a single broadcast including a set ofC−1 point-to-point messages; (ii) a single broadcast including a set of2E₁+E₂ messages; and/or (iii) zero broadcasts, including a set of C−1point-to-point messages. In some embodiments of the present invention,the set of C−1 point-to-point messages includes O(Cm) numbers. In someembodiments of the present invention, a data-mining task includes alatency of c₁. In some embodiments of the present invention, adata-mining task includes a bandwidth of c₂. In some embodiments of thepresent invention, a training set includes m examples. In someembodiments of the present invention, a broadcast operation has aduration log C(c₁+c₂m). In some embodiments of the present invention, abroadcast operation has a duration

${2\;\log\; C\; c_{1}} + {2\; c_{1}\frac{c - 1}{c}{m.}}$In some embodiments of the present invention, y (h,V) is a functioncounting a number of parts in V that h intersects. In some embodimentsof the present invention, E₁ is O(m). In some embodiments of the presentinvention, E₂ is O(C²). In some embodiments of the present invention:E ₁:=Σ_(h∈)

{1:y(h,V)≥c ₁}  (13)

In some embodiments of the present invention:E ₂:=Σ_(≤c<d≤C){1:∃h∈

,y(h,{V _(c) ,V _(d)})=2

y(h,V)<c ₁}  (14)

In some embodiments of the present invention, using zero broadcastscauses an increase in a number of iterations. In some embodiments of thepresent invention, the determination of the number of operations isdependent on whether to parallelize computation. In some embodiments ofthe present invention, parallelization of computation is necessary basedon available memory. In some embodiments of the present invention,parallelization of computation is necessary based on a cost ofdistributing an input. In some embodiments of the present invention,parallelization of computation is necessary based on a set of costs ofvarious properties of a network. In some embodiments of the presentinvention a set of various properties of a network on which a set ofcosts is determined includes, but is not limited to: (i) a networklatency; (ii) a bandwidth of a set of point-to-point connections; and/or(iii) a price of a broadcast. In some embodiments of the presentinvention, selection of an algorithm is performed by a data-mining taskscheduler.

Some embodiments of the present invention implement single valuedecomposition (SVD) of Laplacian matrices. In some embodiments of thepresent invention, a Laplacian matrix is implemented in a clusteringalgorithm. In some embodiments of the present invention, SVD of aLaplacian matrix is implemented in a clustering algorithm. In someembodiments of the present invention, SVD of a Laplacian matrix involvesa set of data descriptors and a set of cost descriptors. In someembodiments of the present invention, SVD of a Laplacian matrix isimplemented for an unsupervised data-mining task. In some embodiments ofthe present invention, SVD of a Laplacian matrix is implemented for acluster algorithm data-mining task. In some embodiments of the presentinvention, a normalized graph Laplacian matrix is represented as:L=D ^(−1/2) WD ^(−1/2)  (15)

In some embodiments of the present invention, matrix W (also matrixW(i,j)) represents a set of weights connecting vertices i and j. In someembodiments of the present invention, matrix D is a diagonal matrix. Insome embodiments of the present invention, matrix D contains a set ofdegrees of graph nodes D (i, j)=Σ_(j)W (i, j).

In some embodiments of the present invention, the SVD of the normalizedLaplacian matrix is rank k. In some embodiments of the presentinvention, the SVD of the normalized Laplacian matrix is associated witha set of graph clustering objective functions. In some embodiments ofthe present invention, a set of graph clustering objective functionsincludes a subset of functions based on random walk models. In someembodiments of the present invention the spectrum of the normalizedLaplacian matrix is related to the spectrum of P=D⁻¹ W. In someembodiments of the present invention, P is a right stochastic matrix. Insome embodiments of the present invention, P is a probability transitionmatrix for matrix W. In some embodiments of the present invention, P isthe probability of traversing from node i to node j. In some embodimentsof the present invention, the probability of traversing from node i tonode j is represented by W (i, j)/D (i, i).

In some embodiments of the present invention, SVD of the normalizedLaplacian matrix approximates a clustering objective. In someembodiments of the present invention, SVD of the normalized Laplacianmatrix approximates a clustering objective to minimize a probability oftraversing between two clusters. In some embodiments of the presentinvention, a clustering objective is represented as:Obj=P[A→B|A]+P[B→A|B]  (16)

In some embodiments of the present invention, P_(ij) represents anelement of a probability transition matrix, P. In some embodiments ofthe present invention, the probability transition matrix is representedas:

$\begin{matrix}{{P\left\lbrack {A\; B} \middle| A \right\rbrack} = \frac{\sum\limits_{{i \in A},{j \in B}}{\pi_{i}^{\infty}P_{i\; j}}}{\pi^{\infty}(A)}} & (17)\end{matrix}$

In some embodiments of the present invention π_(i) ^(∞) represents astationary distribution of a node i. In some embodiments of the presentinvention, π_(i) ^(∞) is based, at least in part, on a probabilitytransition matrix P. In some embodiments of the present invention,π^(∞)(A) represents a sum of a set of stationary distributions of a setof nodes in cluster A. In some embodiments of the present invention, aclustering objective is equivalent to a normalized cut clustering. Insome embodiments of the present invention, a clustering objective iswritten as an equivalent form of a Trace optimization. In someembodiments of the present invention, a random walk model is associatedwith a modified version of an SVD of a normalized Laplacian matrix.

In some embodiments of the present invention, a set of data descriptorsare derived from an eigenvalue distribution of a Laplacian matrix. Insome embodiments of the present invention, an eigenvalue distribution ofa Laplacian matrix is related to a cost descriptor. In some embodimentsof the present invention, a cost descriptor is correlated with arequired memory and to determine a number of data-passes required for adata-mining task. In some embodiments of the present invention, a set ofdata passes are required due to the size of a set of input data. In someembodiments of the present invention, a set of input data does not fitin a memory. In some embodiments of the present invention, computationsinvolving an input matrix read a set of data from a disk. In someembodiments of the present invention, a compressed version of an inputmatrix fits in a memory. In some embodiments of the present invention, acompressed version of an input matrix is associated with a requiredmemory cost. In some embodiments of the present invention, a set ofeigenvalue-computations are well-conditioned. In some embodiments of thepresent invention, a set of eigenvector computations depends on aneigenvalue distribution of a matrix. In some embodiments of the presentinvention, estimate mod 520 estimates an eigenvalue distribution. Insome embodiments of the present invention, estimate mod 520 estimates acost descriptor to compute an SVD of a matrix. In some embodiments ofthe present invention, an eigenvalue distribution is derived by adata-descriptor extractor. In some embodiments of the present invention,a data-descriptor extractor provides a method for estimating a set ofdata descriptors for a data set.

In some embodiments of the present invention, a cost descriptor for aclustering data-mining task is a required memory. In some embodiments ofthe present invention, an association between an eigenvalue distributionand a cost descriptor indicating the required memory size is created. Insome embodiments of the present invention, a required memory to computean SVD of a matrix is determined by the size of a random Gaussianmatrix, R_(G). In some embodiments of the present invention, anassociation between an eigenvalue distribution and a cost descriptor isrepresented as:

$\begin{matrix}{{E{{L - {\hat{L}}_{k}}}_{F}} \leq {\left( {1 + \frac{k}{p - 1}} \right)^{1/2}\left( {\sum\limits_{j > k}\sigma_{j}^{2}} \right)^{1/2}}} & (18)\end{matrix}$

In some embodiments of the present invention, the random Gaussianmatrix, R_(G), has dimensions n×(k+p). In some embodiments of thepresent invention, L represents a Laplacian matrix. In some embodimentsof the present invention, {circumflex over (L)}_(k) represents a rank-kapprocimation of a Laplacian matrix. In some embodiments of the presentinvention, a set of expected value bounds provide an averageapproximation of errors. In some embodiments of the present invention, aset of tail bounds are included. In some embodiments of the presentinvention, a set of errors do not have a high variance. In someembodiments of the present invention, a set of bounds represent abehavior of a clustering algorithm. In some embodiments of the presentinvention, a set bounds are determined with two data passes. In someembodiments of the present invention, a set of bounds associates amemory usage with a quality bound of an SVD approximation.

In some embodiments of the present invention, a number of data passes isused to determine a set of approximation bounds. In some embodiments ofthe present invention, a set of approximation bounds is used todetermine a relationship between an eigenvalue distribution and a numberof data passes. In some embodiments of the present invention, aneigenvalue distribution is a data descriptor. In some embodiments of thepresent invention, a number of data passes is a cost descriptor. In someembodiments of the present invention, a number of data passes isinversely related to a quality of an approximation. In some embodimentsof the present invention, a tradeoff exists between a number of datapasses and a quality of an approximation. In some embodiments of thepresent invention, an approximation bound is represented as:

$\begin{matrix}{{E{{L - {\hat{L}}_{k}}}_{F}} \leq \left\lbrack {{\left( {1 + \sqrt{\frac{k}{p - 1}}} \right)\sigma_{k + 1}^{{2\; q} + 1}} + {\frac{\sqrt[e]{k + p}}{p}\left( {\sum\limits_{j > k}\sigma_{j}^{2{({{2\; q} + 1})}}} \right)^{1/2}}} \right\rbrack^{1/{({{2\; q} + 1})}}} & (19)\end{matrix}$

In some embodiments of the present invention, k+p represents the size ofthe random Gaussian matrix, R_(G). In some embodiments of the presentinvention, k+p represents a set of memory usage requirements. In someembodiments of the present invention, 2q+1 represents a number of datapasses. In some embodiments of the present invention, an end-user isunaware of an eigenvalue distribution of a data set. In some embodimentsof the present invention, an end-user does not have an estimate of aneigenvalue distribution of a data set. In some embodiments of thepresent invention, estimate mod 520 approximates an eigenvaluedistribution of a data set.

FIG. 6 depicts data-mining environment 600. Data-mining environment 600includes: data set 605; set of control values 610; set of taskparameters 615; target to completion 620; job definer 630; CPU costs640; memory costs 645; I/O costs 650; network costs 655; other costs660; cloud interface 665; cost estimates 670; partial learning 680; anddata descriptor extractor 690. In some embodiments of the presentinvention, one or more objects in data-mining environment are notimplemented; for example, in some embodiments of the present invention,data descriptor extractor 690 is not implemented because data set 605does not contain a set of data descriptors. In some embodiments of thepresent invention, one or more objects depicted in data-miningenvironment 600 are omitted; for example, in some embodiments of thepresent invention, a supervised data-mining task is implemented andpartial learning 680 is not necessary and is omitted.

In some embodiments of the present invention, data set 605, set ofcontrol values 610, set of task parameters 615, and/or target tocompletion 620 are provided to job definer 630 as a set of inputs. Insome embodiments of the present invention, data set 605 includes a setof data descriptors. In some embodiments of the present invention, setof control values 610 includes a desired accuracy. In some embodimentsof the present invention, set of task parameters 615 includes analgorithm classifier. In some embodiments of the present invention,target to completion 620 includes a time period in which to complete adata-mining task. In some embodiments of the present invention, a set ofinputs in data-mining environment 600 are represented as a singleobject. In some embodiments of the present invention, job definer 630determines a data-mining task to be performed. In some embodiments ofthe present invention, job definer 630 includes a set of logic tospecify a data-mining task. In some embodiments of the presentinvention, a data-mining task is based, at least in part, on the set ofinputs received by job definer 630. In some embodiments of the presentinvention, job definer 630 selects an algorithm to complete adata-mining task. In some embodiments of the present invention, jobdefiner 630 is adapted to select an algorithm based on a convergencebehavior. In some embodiments of the present invention, a set ofalgorithms for a data-mining task converge at different rates. In someembodiments of the present invention, selection of an algorithm is aseparate object within data-mining environment 600.

In some embodiments of the present invention, partial learning 680 is analgorithm to improve a cost estimate. In some embodiments of the presentinvention, partial learning 680 receives information from job definer630. In some embodiments of the present invention, partial learning 680analyzes a set of prior cost estimates for prior data-mining tasks. Insome embodiments of the present invention, partial learning 680 analyzesa set of actual costs for prior data-mining tasks. In some embodimentsof the present invention, partial learning 680 determines a set ofoverestimates and a set of underestimates based on a set of priordata-mining tasks. In some embodiments of the present invention, partiallearning 680 determines a set of discrepancies for a set of differentcost breakdowns. In some embodiments of the present invention, partiallearning 680 provides a set of scaling factors for a set of differentcost breakdowns. In some embodiments of the present invention, a set ofscaling factors provided by partial learning 680 are incorporated into aset of future cost estimates by job definer 630. In some embodiments ofthe present invention, partial learning 680 builds on prior costestimates for prior data-mining tasks to improve a current costestimate. In some embodiments of the present invention, partial learning680 refines a set of estimation parameters based, at least in part, onprior cost estimates.

In some embodiments of the present invention, data descriptor extractor690 extracts a set of data descriptors from a data set. In someembodiments of the present invention, additional computational costs areincurred by data descriptor extractor 690. In some embodiments of thepresent invention, an estimate of additional computational costsincurred by data descriptor extractor 690 is required. In someembodiments of the present invention, data descriptor extractor 690includes an algorithm to estimate a set of data descriptors in a dataset. In some embodiments of the present invention, a data set includesonly a set of data descriptors. In some embodiments of the presentinvention, a set of data descriptors are used to estimate acomputational cost based, at least in part, on a complexity of the setof data descriptors.

In some embodiments of the present invention, a set of costs areestimated based, at least in part, on a selected algorithm. In someembodiments of the present invention, a set of costs estimation objectsare represented as a single object within data-mining environment 600.In some embodiments of the present invention, a set of costs are brokendown based on usage area. In some embodiments of the present invention,costs are separated into one of the following categories: (i) CPU costs640; (ii) memory costs 645; (iii) I/O costs 650; (iv) network costs 655;and/or (v) other costs 660. In some embodiments of the presentinvention, one or more costs are combined into a single object. In someembodiments of the present invention, a cost estimate is estimatedbased, at least in part, on an algorithm received from job definer 630.In some embodiments of the present invention, a cost object bases anestimate on a set of data descriptors from data set 605.

In some embodiments of the present invention, cloud interface 665interacts with a set of cloud service providers. In some embodiments ofthe present invention, cloud interface 665 interacts with an applicationprogramming interface (API) for a cloud server. In some embodiments ofthe present invention, cloud interface 665 is an API for a cloud server.In some embodiments of the present invention, a cloud service providerestimates a cloud cost for a data-mining task. In some embodiments ofthe present invention, cloud interface 665 estimates a cloud cost for adata-mining task. In some embodiments of the present invention, cloudinterface 665 bases the estimate, at least in part, on one or more of:(i) data set 605; (ii) set of control values 610; (iii) set of taskparameters 615; and/or (iv) target to completion 620.

In some embodiments of the present invention, cost estimates 670compiles a set of costs from data-mining environment 600. In someembodiments of the present invention, cost estimates 670 determines afinal cost for a data-mining task.

FIG. 7 depicts data-mining environment 700. In some embodiments of thepresent invention, data-mining environment 700 includes optional objectsthat are implemented with data-mining environment 600. Data-miningenvironment 700 includes: data set 605; set of control values 610; setof task parameters 615; target to completion 620; semantic problemdescription 710; and rule engine 720.

In some embodiments of the present invention, data-mining environment700 provides additional input for data-mining environment 600. In someembodiments of the present invention, semantic problem description 710is received as an input. In some embodiments of the present invention,semantic problem description 710 is a plain language description of adata-mining task. In some embodiments of the present invention, semanticproblem description 710 is provided by a user. In some embodiments ofthe present invention, semantic problem description 710 provides ahigh-level description of a data-mining task.

In some embodiments of the present invention, rule engine 720 specifiesa set of requirements for a data-mining task. In some embodiments of thepresent invention, rule engine 720 determines a set of requirements forother inputs. In some embodiments of the present invention, rule engine720 converts semantic problem description 710 to a format understandableby data-mining environment 600. In some embodiments of the presentinvention, rule engine 720 includes a set of frequent parameters. Insome embodiments of the present invention, data-mining environment 700increases ease of user interaction with data-mining environment 600.

FIG. 8 depicts data-mining environment 800. In some embodiments of thepresent invention, data-mining environment 800 includes optional objectsthat are implemented with data-mining environment 600. Data-miningenvironment 800 includes: CPU costs 640; memory costs 645; I/O costs650; network costs 655; other costs 660; cloud scheduler 810; minimalcost estimates 820; and optimal deployment strategy 830.

In some embodiments of the present invention, cloud scheduler 810receives a set of information from one or more of: CPU costs 640; memorycosts 645; I/O costs 650; network costs 655; and/or other costs 660. Insome embodiments of the present invention, cloud scheduler 810 providesa set of processing packages for a data-mining task. In some embodimentsof the present invention, a set of processing packages is based, atleast in part, on a set of requirements of a data-mining task. In someembodiments of the present invention, a set of requirements of adata-mining task include, but are not limited to: (i) a CPU powerrequirement; (ii) an I/O device load; and/or (iii) a network load. Insome embodiments of the present invention, cloud scheduler 810 providesa subset of processing packages that are not viable. In some embodimentsof the present invention, cloud scheduler 810 provides a subset ofprocessing packages that are viable. In some embodiments of the presentinvention, cloud scheduler 810 selects a processing package from asubset of processing packages that are viable. In some embodiments ofthe present invention, cloud scheduler 810 selects a processing packagebased, at least in part, on a set of control variables. In someembodiments of the present invention, cloud scheduler 810 receives aprocessing package as an input.

FIG. 9 depicts an example breakdown of job definer 230 into a set ofconstituent objects. Job definer 230 includes: data statistics estimator931; flow composer algorithm 932; set of flows 934; and flow-based costestimator 935.

In some embodiments of the present invention, data statistics estimator931 interacts with other objects in data-mining environment 600. In someembodiments of the present invention, data statistics estimator 931receives a set of information from one or more of: data set 605; set ofcontrol values 610; set of task parameters 615; and/or target tocompletion 620. In some embodiments of the present invention, datastatistics estimator 931 receives information from partial learning 680and/or data descriptor extractor 690. In some embodiments of the presentinvention, data statistics estimator 931 sends information to partiallearning 680 and/or data descriptor extractor 690. In some embodimentsof the present invention data statistics estimator 931 estimates a setof properties associated with a data set. In some embodiments of thepresent invention, a set of properties associated with a data setincludes, but is not limited to: (i) a sparseness of data values; (ii)an independence of data; (iii) a set of eigenvectors; and/or (iv) anumber of non-zero data values. In some embodiments of the presentinvention, a set of properties associated with a data set is correlatedwith a computational effort required for a data-mining task. In someembodiments of the present invention, data statistics estimator 931improves a cost estimation.

In some embodiments of the present invention, flow composer algorithm932 receives information from data statistics estimator 931. In someembodiments of the present invention, develops a set of flows to performa data-mining process. In some embodiments of the present invention,different data-mining tasks require a different number of flows. In someembodiments of the present invention, different flows use differentalgorithms to estimate a set of costs. In some embodiments of thepresent invention, different flows use different data descriptors toestimate a set of costs.

In some embodiments of the present invention, set of flows 934 includesone or more flows to determine aspects of a data-mining task. In someembodiments of the present invention, a number of flows included in setof flows 934 is determined by flow composer algorithm 932. In someembodiments of the present invention, different flows in set of flows934 determine different aspects of a data-mining task. In someembodiments of the present invention, a plurality of flows included inset of flows 934 use a single algorithm to determine a single aspect ofa data-mining task. In some embodiments of the present invention, set offlows 934 determines a set of cost estimates. In some embodiments of thepresent invention, set of flows 934 determines a set of component valuesmaking up a set of cost estimates. In some embodiments of the presentinvention, different flows in set of flows 934 analyze algorithms fordifferent methods of completing a data-mining task. In some embodimentsof the present invention, different flows in set of flows 934 analyzealgorithms associated with different parts of a data-mining task. Insome embodiments of the present invention, set of flows 934 analyzes adata set with a set of control variable. In some embodiments of thepresent invention, set of flows 934 analyzes a set of control variablesat different values. In some embodiments of the present invention, setof flows 934 varies a processing speed and/or an accuracy.

In some embodiments of the present invention, flow-based cost estimator935 receives a set of values from set of flows 934. In some embodimentsof the present invention, flow-based cost estimator 935 estimates a costassociated with a data-mining task. In some embodiments of the presentinvention. In some embodiments of the present invention, flow-based costestimator 935 selects an algorithm, based in part on a value provided byset of flows 934. In some embodiments of the present invention,flow-based cost estimator 935 determines a set of costs for adata-mining task based on a set of algorithms. In some embodiments ofthe present invention, flow-based cost estimator 935 selects analgorithm based on a lowest cost. In some embodiments of the presentinvention, flow-based cost estimator 935 provides a graphical display ofa set of values received from set of flows 934. In some embodiments ofthe present invention, flow-based cost estimator 935 providesinformation to cloud interface 665. In some embodiments of the presentinvention, CPU costs 640, memory costs 645, I/O costs 650, network costs655, and/or other costs 660 are included in job definer 630. In someembodiments of the present invention, CPU costs 640, memory costs 645,I/O costs 650, network costs 655, and/or other costs 660 are omitted.

Some embodiments of the present invention estimate a set if costs ofassociated with a data-mining algorithm run on a cloud infrastructure.Some embodiments of the present invention may include one, or more, ofthe following features, characteristics, and/or advantages: (i) analgorithmic job definer estimating system requirements for running adata-mining task over a data set with result targets; (ii) a datadescriptor tool that can be used to interact with the user in order tofurther specify the properties of the targeted input data set; (iii) apartial learning tool that can be used to estimate system requirementsthrough runs on sub-sample of the target data; and/or (iv) a costfeedback UI presenting the result of the cost evaluation to the end-useror inquiring application.

Some embodiments of the present invention may include one, or more, ofthe following features, characteristics, and/or advantages: (i)extracting system requirements to perform a data-mining task over atarget data set; pricing the cost of deploying such data-mining taskover a cloud infrastructure; (ii) estimating the cost of performing adata-mining task using partial learning or data descriptors; and/or(iii) creating a set of estimates at different levels of quality oraccuracy, dependent on system requirements.

Some embodiments of the present invention involve provision of“data-mining as a service.” In some embodiments of the presentinvention, “data-mining as a service” is a cloud-based service. Someembodiments of the present invention bridge a gap between a data-miningtask and a computational effort required to perform the data-miningtask. In some embodiments of the present invention, a cost estimate fora data-mining task is provided, based, at least in part, on anestimation of a set of computational resources required to perform thedata-mining task. In some embodiments of the present invention, a costestimate for a data-mining task is defined in terms of a set ofcomputational tasks. In some embodiments of the present invention, acost estimate for a data-mining task is defined in terms of a setcomputational resources to be used.

In some embodiments of the present invention, a set of costs isestimated by deriving a set of cost descriptors. In some embodiments ofthe present invention, a set of cost descriptors includes, but is notlimited to, a set of information about a number of iterations to computea data-mining task. In some embodiments of the present invention, a setof cost descriptors includes a set of information about a complexity ofa set of operations performed per iteration. In some embodiments of thepresent invention, a number of iterations is a number of data passesacross all coordinates. In some embodiments of the present invention, anumber of iterations depends on a number of computing entities executinga data-mining task. In some embodiments of the present invention, anumber of iterations depends on a desired accuracy. In some embodimentsof the present invention, a set of information regarding a complexity ofa set of operations per iteration is a number of arithmetic operationsperformed during a data pass. In some embodiments of the presentinvention, a number of arithmetic operations performed during a datapass is a function of a number of computing entities executing adata-mining task. In some embodiments of the present invention, a set ofcost descriptors includes a set of information about a number ofbroadcast operations performed per data pass. In some embodiments of thepresent invention, a set of cost descriptors includes a set ofinformation about a number of bytes being transmitted in a broadcastoperation. In some embodiments of the present invention, a set of costdescriptors includes a set of information about a number ofpoint-to-point communication operations performed during a data pass. Insome embodiments of the present invention, a set of cost descriptorsincludes a set of information about a number of bytes transmitted in acommunication operation. Some embodiments of the present inventionemploy segmentation of a data-mining task into multiple blocks (e.g.,CPU cost, memory cost, I/O cost, network cost). In some embodiments ofthe present invention, segmentation of a data-mining task into multipleblocks aids in determining a computational complexity of a data-miningtask. In some embodiments of the present invention, determining acomputational complexity of a data-mining task improves an estimation ofa computational effort. In some embodiments of the present invention,improving an estimation of a computational effort improves an estimationof a costs of a data-mining task.

In some embodiments of the present invention, a set of control valuesincludes a desired accuracy. In some embodiments of the presentinvention, a set of control values includes a desired duration tocompute a computational resource estimation. In some embodiments of thepresent invention, a set of control values influences a computationaleffort of a data-mining task. In some embodiments of the presentinvention, an estimation accuracy is linked to a number of iterationsperformed on a data set to obtain a desired accuracy. In someembodiments of the present invention, a computing duration influences anumber of employed computing entities. In some embodiments of thepresent invention, a set of control values includes a start time and/oran end time for a data-mining task. In some embodiments of the presentinvention, a start time and/or an end time for a data-mining taskinfluences a task cost. In some embodiments of the present invention,data center loads vary by time of day. In some embodiments of thepresent invention, costs for computing entities are reduced during timesof reduced data center load. In some embodiments of the presentinvention, a subset of a set of control values impacts an estimationresult.

In some embodiments of the present invention, a task parameter includesan algorithm classifier. In some embodiments of the present invention,an algorithm classifier defines a type of data-mining task to beperformed. In some embodiments of the present invention, an algorithmclassifier stores a classification of a data-mining task as an elementof data. In some embodiments of the present invention, a classificationindicates that a data-mining task is one of: a regression task; aclassification task; or a clustering task.

In some embodiments of the present invention, a loss function type isreceived if a data-mining task is classified as regression task. In someembodiments of the present invention, a loss function type is receivedif a data-mining task is classified as classification task. In someembodiments of the present invention, a loss function measures asimilarity of a given data set to a predicted data. In some embodimentsof the present invention, a goal of a data-mining process is to minimizea loss function. In some embodiments of the present invention,minimizing a loss function reduces an estimation error. In someembodiments of the present invention, minimizing a loss functionimproves an estimation accuracy. In some embodiments of the presentinvention, a loss function type indicates a loss function as one of: asquare loss function; a logarithmic loss function; or a hinge squareloss function.

In some embodiments of the present invention, a regularization functionis received. In some embodiments of the present invention, aregularization function provides a set of additional information for anestimation process. In some embodiments of the present invention, a setof additional information improves an ability to solve an ill-posedproblem. In some embodiments of the present invention, a set ofadditional information improves an ability to prevent over-fitting. Insome embodiments of the present invention, a set of additionalinformation improves an ability to incorporate domain knowledge. In someembodiments of the present invention, an L₀ norm is used to interpret asparse data set. In some embodiments of the present invention, an L₁norm is used to interpret a sparse data set. In some embodiments of thepresent invention, an estimation accuracy is increased by penalizing asubset of data values. In some embodiments of the present invention, asubset of data values is penalized by a regularization function.

In some embodiments of the present invention, a clustering function(similarity function) is received for a clustering data-mining task. Insome embodiments of the present invention, a clustering functionprovides a cost to partition a set of data into multiple data clusters.In some embodiments of the present invention, a clustering functionprovides a cost to include a subset of elements in a cluster. In someembodiments of the present invention, a clustering function provides acost to cluster a subset of elements within a list of designated subsetsof elements. In some embodiments of the present invention, a clusteringfunction (similarity function) is a k-means function. In someembodiments of the present invention, a clustering function (similarityfunction) is a normalized cut function. In some embodiments of thepresent invention, a set of data is grouped (clustered) such that anintra-group (intra-cluster) similarity is greater than an inter-group(inter-cluster) similarity.

In some embodiments of the present invention, a set of data descriptorsassociated with a data set are received. In some embodiments of thepresent invention, a set of data descriptors includes a set ofproperties defining characteristics of a data set. In some embodimentsof the present invention, a set of properties defining characteristicsof a data set influences a computational effort of a data-mining task.In some embodiments of the present invention, a set of data descriptorsincludes, but is not limited to: (i) a number of features (dimension) ofa data set; (ii) a number of described items in a data set; (iii) for adata set with a block structure, a number of blocks in the data set;(iv) a degree of partial separability of a loss function (e.g., a k-cutfunction in a hypergraph); (v) a square of a largest eigenvalue,L=σ_(max) ²(A); (vi) a condition number σ_(max)(A)/σ_(min)(A); (vii) aset of other properties of a spectrum of an item-feature matrix; and/or(viii) a maximum number of non-zero values in a column or block of amatrix. In some embodiments of the present invention, a condition numberis used to determine a number of iterations to be performed.

In some embodiments of the present invention, an algorithm is selectedfrom a set of algorithms for estimating a computational effort of adata-mining task. In some embodiments of the present invention,selection of an algorithm is based on a data set. In some embodiments ofthe present invention, selection of an algorithm is based on a set ofdata descriptors. In some embodiments of the present invention,selection of an algorithm is based on a set of control values. In someembodiments of the present invention, selection of an algorithm is basedon a set of task parameters. In some embodiments of the presentinvention, selection of an algorithm is based on an assessment of a setof computational criteria of a data-mining task. In some embodiments ofthe present invention, an algorithm is selected based, at least in part,on a set of input parameters. In some embodiments of the presentinvention, a set of logic is provided for selecting an algorithm. Insome embodiments of the present invention, a set of logic is adapted tochoose a most appropriate algorithm for solving a data-mining task. Insome embodiments of the present invention, an algorithm selection isautomated. In some embodiments of the present invention, automating analgorithm selection enhances a user-friendliness of a data-mining task.

Some embodiments of the present invention include one or more of thefollowing features: (i) providing information regarding a set ofcomputational resources required to perform a data-mining task; (ii)providing information regarding a set of control values to a deploymentscheduler; (iii) employing a said deployment scheduler that includes aset of information about a plurality of distributed computing systems;(iv) selecting a set of distributed computing systems based on a set ofcomputational resource information; (v) selecting a set of distributedcomputing systems based on a set of control values; and/or (vi)returning a set of information regarding a cost to deploy a data-miningtask on a distributed computing system.

In some embodiments of the present invention, a deployment schedulerinvestigates a suitable deployment strategy for performing a data-miningtask. In some embodiments of the present invention, a distributedcomputing system (a cloud environment) is selected to ensure processingof a data-mining task at a lowest cost. In some embodiments of thepresent invention, a distributed computing system is selected to ensurea fastest processing of a data-mining task. In some embodiments of thepresent invention, a distributed computing system is selected to ensurea desired security for processing of a data-mining task. In someembodiments of the present invention, information about the selection ofa distributed computing system is displayed on a graphical userinterface.

Some embodiments of the present invention include one or more of thefollowing features: (i) receiving a high-level task description of adata-mining task; (ii) deriving a set of parameters of a data-miningtask based on a high-level task description using a task definitionapplication; and/or (iii) estimating a set of computational resourcesrequired to perform a data-mining task based, at least in part, on a setof parameters.

In some embodiments of the present invention, an interface is providedto increase a usability of a data-mining task. In some embodiments ofthe present invention, an interface is adapted to receive a high-leveldescription of a data-mining task in a user-friendly syntax. In someembodiments of the present invention data-mining task is refined basedon a high-level description of the data-mining task. In some embodimentsof the present invention, refining of a data-mining task includes one ormore of: (i) selecting a data-mining algorithms; (ii) pre-processing adata set; (iii) deriving a set of data descriptors; and/or (iv) derivinga set of cost descriptors. In some embodiments of the present invention,results of a refining process are inputs to an estimate of computationalresources required for a data-mining task. In some embodiments of thepresent invention, results of a refining process are inputs to anestimate of computational costs required for a data-mining task. In someembodiments of the present invention, automating a refining processenhances a user-friendliness of a data-mining task.

Some embodiments of the present invention provide for an estimate of thecomputational resources for running a data-mining task. Some embodimentsof the present invention are freely combined with one another each otherif they are not mutually exclusive.

Some embodiments of the present invention provide for estimates ofcomputational resources for running a data-mining task over adistributed computing system. Some embodiments of the present inventionprovide for estimates of computational resources for running adata-mining task over a cloud computing environment. Some embodiments ofthe present invention includes one or more steps, including, but notlimited to: (i) receiving a data set, based on which the data-miningtask is performed an/or data descriptors describing or bounding featuresof the data set relevant for estimating computational resources forrunning the data-mining task; (ii) receiving one or more control valuesfor said data-mining task; (iii) receiving one or more task parameters,said task parameters defining the data-mining task to be performed onsaid dataset; and/or (iv) estimating the computational resources forperforming the data-mining task over the distributed computing systembased on the received data set or the received data descriptors, the oneor more control values and the one or more task parameters.

The actual workflow of embodiments of the present invention need notfollow the sequence of steps above or listed herein. In some embodimentsof the present invention, a set of task parameters is a first input; adata set is a second input; and a set of control values is a thirdinput; then an estimate is created.

Some embodiments of the present invention recognize the following facts,potential problems, and/or potential areas for improvement with respectto the current state of the art: (i) data-mining tasks employnon-trivial, iterative algorithms; and/or (ii) resource consumption forcost estimates is an additional variable.

Some embodiments of the present invention include one or more of thefollowing features related to a computer-implemented method to estimatecomputational resources for running a data-mining task over adistributed computing system: (i) providing a set of control variables(accuracy, time to completion, other) for data-mining tasks, the controlvariables affecting amount of computational resources needed for thedata-mining tasks; (ii) receiving a description of the data-mining taskto be performed indicating a data set, a task to be performed on thatdataset and possible additional variables from a user; (iii) receivingdata descriptors for the data set, the data descriptors describing orbounding key features of the dataset relevant for estimating thecomputational resources for data-mining tasks; (iv) defining a jobperforming the data-mining task and estimating computational resourcesfor performing that task over the distributed computing system, based onthe data-mining tasks, and dimensions and availability of thedistributed computing system, as a function of the data descriptors andcontrol variables; and/or (v) presenting the estimated computationalresources to the user in function of control variables.

Some embodiments of the present invention relates to the pricing ofdata-mining tasks in the cloud. Some embodiments of the presentinvention bridge a gap between infrastructure costs for a cloud providerand accuracy results for a customer. Some embodiments of the presentinvention include one or more of the following features: (i) enablingautomated price setting for performance of a set of data-mining tasksover a cloud infrastructure; (ii) estimating a set of system resourcesand hardware resources required to perform a data-mining job suite witha target accuracy, time to completion, or other data-mining relatedrequirements; (iii) estimating and/or improving estimates of costsassociated with running a data-mining job-suite; and/or (iv) setting andpublishing estimated prices to a prospective customer.

Some embodiments of the present invention include one or more of thefollowing features: (i) asking a user for a description of a data-miningtask; (ii) producing a set of execution plans for performing adata-mining task and a set of estimated cost metrics for each executionplan; (iii) asking a user for clarifications on a data set or a targettask; (iv) extracting further data descriptors (which describe keyfeatures of the targeted data set) that affect a set of costdescriptors; (v) presenting a set of costs to a user as a function of aset of data-mining parameters; (vi) receiving a set of high-level tagsdescribing a target application; (vii) suggesting a set of requirementsfor a data-mining task; (viii) offering a set of packages depending onsystem and availability requirements of a target application; (ix)selecting a package and deployment strategy to deploy the targeteddata-mining task; (x) presenting a user with a set of costs anddeployment strategies.

Possible combinations of features described above can be the followingitems:

1. A computer-implemented method for estimating computational resourcesfor running a data-mining task over a distributed computing system, themethod comprising: (i) receiving a data set, based on which thedata-mining task is performed and/or data descriptors describing orbounding features of the data set relevant for estimating computationalresources for running the data-mining task; (ii) receiving one or morecontrol values for said data-mining task; (iii) receiving one or moretask parameters, said task parameters defining the data-mining task tobe performed on said dataset; and (iv) estimating the computationalresources for performing the data-mining task over the distributedcomputing system based on the received data set or the received datadescriptors, the one or more control values and the one or more taskparameters.

2. The computer-implemented method of item 1, the method furthercomprising: estimating costs of the data-mining task based on theestimated computational resources required for performing thedata-mining task.

3. The computer-implemented method of item 2, the method estimating thecosts by deriving cost descriptors, said cost descriptors at leastincluding information regarding the number of iterations required forcomputing the data-mining task and information regarding the complexityof operations per each iteration.

4. The computer-implemented method of item 2 or 3, the method furthercomprising: providing a set of cost data based on a value range of oneor more control variables, said set of cost data comprising multiplecost values being associated with certain values of said one or morecontrol variables.

5. The computer-implemented method of anyone of the preceding items, theone or more control values being the desired accuracy of estimating thecomputational resources and/or the duration for computing thecomputational resource estimation.

6. The computer-implemented method of anyone of the preceding items, thetask parameter comprising an algorithm classifier, said algorithmclassifier defining the type of data-mining task to be performed on thedata.

7. The computer-implemented method of item 6, the algorithm classifierclassifying the data-mining task as being a regression task,classification task or clustering task.

8. The computer-implemented method of item 7, the method furtherreceiving a loss function type in case of a regression task orclassification task.

9. The computer-implemented method of item 8, the method furtherreceiving a regularization function.

10. The computer-implemented method of item 7, the method furtherreceiving a clustering or similarity function in case of a clusteringtask, said clustering or similarity function evaluating a partitioningof data included in the data set into multiple data clusters.

11. The computer-implemented method of anyone of the preceding items,the method further receiving one or more data properties associated withthe data included in the data set, said data properties definingcharacteristics of the data influencing the computational effort ofrunning the data-mining task.

12. The computer-implemented method of anyone of the preceding items,the method further providing: extracting one or more data descriptorsout of the data set by applying an data descriptor extracting algorithmon the data set.

13. The computer-implemented method of anyone of the preceding items,the method further providing: selecting an algorithm out of a pluralityof algorithms for estimating the computational effort of the data-miningtask based on the received data set and/or the received datadescriptors, the one or more control values and the one or more taskparameters by assessing computational criteria of the data-mining task.

14. The computer-implemented method of anyone of the preceding items,the method further comprising: (i) providing information regarding thecomputational resources required for performing the data-mining task andone or more control values to a deployment scheduler, said deploymentscheduler comprising information regarding a plurality of distributedcomputing systems being adapted to perform data-mining tasks; (ii)selecting one or more distributed computing systems based on thecomputational resource information and the one or more control values;and (iii) returning information regarding the costs of deploying thedata-mining task on a certain distributed computing system.

15. The computer-implemented method of item 14, the method furtherreturning information regarding a deployment strategy for processing thedata-mining task on a certain distributed computing system.

16. The computer-implemented method of anyone of the preceding items 2to 15, the method further comprising: (i) receiving feedback informationcomprising information regarding previously performed cost estimationsof data-mining tasks and real costs occurred when deploying saiddata-mining task; and (ii) refining the cost estimation of a currentdata-mining task by using said feedback information.

17. The computer-implemented method of anyone of the preceding items,the method further comprising: (i) calculating multiple process flowsfor processing the data-mining task; (ii) estimating for each processflow the computational effort for processing the data-mining task; (iii)estimating for each process flow the costs for processing thedata-mining task on a distributed computing system; and (iv) presentingthe user with the multiple process flow costs or choosing the processflow with the lowest costs for processing the data-mining task.

18. The computer-implemented method of anyone of the preceding items,the method further comprising: (i) receiving a high-level taskdescription of the data-mining task to be performed; (ii) derivingparameters of the data-mining task based on said high-level taskdescription by using a task definition application; and (iii) estimatingthe computational resources for performing the data-mining task based onsaid parameters provided by the task definition application.

19. The computer-implemented method of item 18, the task definitionapplication deriving parameters indicating one or more suitabledata-mining algorithms, one or more data descriptors and/or one or morecost descriptors.

20. A computer program product for estimating computational resourcesfor running a data-mining task over a distributed computing system, thecomputer program product comprising a computer readable storage mediumhaving program instructions embodied therewith, the program instructionsexecutable by a processor to cause the processor to execute the methodcomprising: (i) receiving a data set, based on which the data-miningtask is performed and/or data descriptors describing or boundingfeatures of the data set relevant for estimating computational resourcesfor running the data-mining task; (ii) receiving one or more controlvalues for said data-mining task; (iii) receiving one or more taskparameters, said task parameters defining the data-mining task to beperformed on said dataset; (iv) estimating the computational resourcesfor performing the data-mining task over the distributed computingsystem based on the received data set or the received data descriptors,the one or more control values and the one or more task parameters.

21. A computer-implemented method for estimating computational resourcesfor running a data-mining task over a distributed computing system, themethod comprising: (i) receiving a high level task description of thedata-mining task to be performed; (ii) receiving one or more controlvalues for said data-mining task; (iii) executing a task definitionapplication, said task definition application providing parameters forthe data-mining task based on the a high level task description of thedata-mining task to be performed; and (iv) estimating the computationalresources for performing the data-mining task over the distributedcomputing system based on said parameters and the one or more controlvalues.

22. The computer-implemented method of item 21, the method furthercomprising: estimating costs of the data-mining task based on theestimated computational resources required for performing thedata-mining task.

23. The computer-implemented method of item 22, the method estimatingthe costs by deriving cost descriptors, said cost descriptors at leastincluding information regarding the number of iterations required forcomputing the data-mining task and information regarding the complexityof operations per each iteration.

24. A computer program product for estimating computational resourcesfor running a data-mining task over a distributed computing system, thecomputer program product comprising a computer readable storage mediumhaving program instructions embodied therewith, the program instructionsexecutable by a processor to cause the processor to execute the methodcomprising: (i) receiving a high level task description of thedata-mining task to be performed; (ii) receiving one or more controlvalues for said data-mining task; (iii) executing a task definitionapplication, said task definition application providing parameters forthe data-mining task based on the a high level task description of thedata-mining task to be performed; and (iv) estimating the computationalresources for performing the data-mining task over the distributedcomputing system based on said parameters and the one or more controlvalues.

25. A system for estimating computational resources for running adata-mining task over a distributed computing system, the systemcomprising: (i) an interface for receiving a data set, based on whichthe data-mining task is performed and/or data descriptors describing orbounding features of the data set relevant for estimating computationalresources for running the data-mining task; (ii) an interface forreceiving one or more control values for said data-mining task; (iii) aninterface for receiving one or more task parameters, said taskparameters defining the data-mining task to be performed on saiddataset; (iv) a data processing module being adapted to estimate thecomputational resources for performing the data-mining task over thedistributed computing system based on the received data set or thereceived data descriptors, the one or more control values and the one ormore task parameters.

26. A method comprising: (i) receiving a set of task parameters, the setof task parameters defining a target data set and a data-mining task;(ii) receiving a set of control values, the set of control valuesdescribing the data-mining task; (iii) receiving a set of datadescriptors, the set of data descriptors describing the target data set;and (iv) estimating a set of computational resources required to performthe data-mining task over a distributed computing system based at leastin part on the set of task parameters, the set of control values, theset of data descriptors, and an availability of the distributedcomputing system; wherein: at least the estimating step is performed bycomputer software running on computer hardware.

27. The method of item 26, further comprising: (i) generating anestimated cost required to perform the data-mining task by deriving aset of cost descriptors, the set of cost descriptors including: a numberof compute iterations required to perform the data-mining task, and acomplexity level of each compute iteration.

28. The method of item 27, further comprising: reporting the estimatedcost required to perform the data-mining task.

29. The method of item 26, further comprising: defining a type of thedata-mining task; wherein: the type is one of a regression task, aclassification task, and a clustering task.

30. The method of item 29, further comprising: receiving, for aclustering task type, a clustering function for evaluating apartitioning of data included in the target data set into a plurality ofdata clusters.

31. The method of item 26, further comprising: (i) receiving ahigh-level task description of the data-mining task; (ii) deriving a setof derived parameters of the data-mining task based on the high-leveltask description using a task definition application; and (iii)estimating the computational resources for performing the data-miningtask based on the set of derived parameters.

32. A computer program product comprising a computer readable storagemedium having stored thereon: (i) first instructions executable by adevice to cause the device to receive a set of task parameters, the setof task parameters defining a target data set and a data-mining task;(ii) second instructions executable by a device to cause the device toreceive a set of control values, the set of control values describingthe data-mining task; (iii) third instructions executable by a device tocause the device to receive a set of data descriptors, the set of datadescriptors describing the target data set; and (iv) fourth instructionsexecutable by a device to cause the device to estimate a set ofcomputational resources required to perform the data-mining task over adistributed computing system based at least in part on the set of taskparameters, the set of control values, the set of data descriptors, andan availability of the distributed computing system.

33. The computer program product of item 32, further comprising: fifthinstructions executable by a device to cause the device to generate anestimated cost required to perform the data-mining task by deriving aset of cost descriptors, the set of cost descriptors including: a numberof compute iterations required to perform the data-mining task, and acomplexity level of each compute iteration.

34. The computer program product of item 33, further comprising: sixthinstructions executable by a device to cause the device to report theestimated cost required to perform the data-mining task.

35. The computer program product of item 32, further comprising: fifthinstructions executable by a device to cause the device to define a typeof the data-mining task; wherein: the type is one of a regression task,a classification task, and a clustering task.

36. The computer program product of item 35, further comprising: sixthinstructions executable by a device to cause the device to receive, fora clustering task type, a clustering function for evaluating apartitioning of data included in the target data set into a plurality ofdata clusters.

37. The computer program product of item 32, further comprising: (i)fifth instructions executable by a device to cause the device to receivea high-level task description of the data-mining task; (ii) sixthinstructions executable by a device to cause the device to derive a setof derived parameters of the data-mining task based on the high-leveltask description using a task definition application; and (iii) seventhinstructions executable by a device to cause the device to estimate thecomputational resources for performing the data-mining task based on theset of derived parameters.

38. A computer system comprising: a processor set; and a computerreadable storage medium; wherein: the processor set is structured,located, connected, and/or programmed to execute instructions stored onthe computer readable storage medium; and the instructions include: (i)first instructions executable by a device to cause the device to receivea set of task parameters, the set of task parameters defining a targetdata set and a data-mining task; (ii) second instructions executable bya device to cause the device to receive a set of control values, the setof control values describing the data-mining task; (iii) thirdinstructions executable by a device to cause the device to receive a setof data descriptors, the set of data descriptors describing the targetdata set; and (iv) fourth instructions executable by a device to causethe device to estimate a set of computational resources required toperform the data-mining task over a distributed computing system basedat least in part on the set of task parameters, the set of controlvalues, the set of data descriptors, and an availability of thedistributed computing system.

39. The computer system of item 38, further comprising: fifthinstructions executable by a device to cause the device to generate anestimated cost required to perform the data-mining task by deriving aset of cost descriptors, the set of cost descriptors including: a numberof compute iterations required to perform the data-mining task, and acomplexity level of each compute iteration.

40. The computer system of item 39, further comprising: sixthinstructions executable by a device to cause the device to report theestimated cost required to perform the data-mining task.

41. The computer system of item 38, further comprising: fifthinstructions executable by a device to cause the device to define a typeof the data-mining task; wherein: the type is one of a regression task,a classification task, and a clustering task.

42. The computer system of item 41, further comprising: sixthinstructions executable by a device to cause the device to receive, fora clustering task type, a clustering function for evaluating apartitioning of data included in the target data set into a plurality ofdata clusters.

43. The computer system of claim 38, further comprising: (i) fifthinstructions executable by a device to cause the device to receive ahigh-level task description of the data-mining task; (ii) sixthinstructions executable by a device to cause the device to derive a setof derived parameters of the data-mining task based on the high-leveltask description using a task definition application; and (iii) seventhinstructions executable by a device to cause the device to estimate thecomputational resources for performing the data-mining task based on theset of derived parameters.

44. A method comprising: (i) receiving a data set; (ii) receiving a setof control values; (iii) receiving a set of data-mining task parameters;and (iv) estimating a set of computational resources required to performa data-mining task; wherein: the set of control values describes thedata-mining task; the set of data-mining task parameters defines thedata set and the data-mining task; the set of computational resources isbased at least in part on the data set, the set of control values, andthe set of data-mining task parameters; and at least the estimating stepis performed by computer software running on computer hardware.

45. The method of item 44, wherein: the set of control values includesan accuracy and a temporal duration; the accuracy is an estimation ofthe set of computational resources; and the temporal duration is a timeperiod to perform the data-mining task.

46. The method of item 44, wherein estimating the set of computationalresources includes: (i) recalling a set of prior computational resourceestimates; and (ii) receiving a set of prior computational resourcecosts; wherein: the set of prior computational resource costscorresponds to the set of prior computational resource estimates.

47. The method of item 44, wherein estimating the set of computationalresources includes: (i) estimating a set of process flows for thedata-mining task; and (ii) estimating a set of computational resourcesrequired for each process flow in the set of process flows; wherein:different flows in the set of process flows represent different mannersof completing the data-mining task.

48. A computer system comprising: a processor set; and a computerreadable storage medium; wherein: the processor set is structured,located, connected, and/or programmed to execute instructions stored onthe computer readable storage medium; and the instructions include: (i)first instructions executable by a device to cause the device to receivea data set; (ii) second instructions executable by a device to cause thedevice to receive a set of control values; (iii) third instructionsexecutable by a device to cause the device to receive a set ofdata-mining task parameters; and (iv) fourth instructions executable bya device to cause the device to estimate a set of computationalresources required to perform a data-mining task; wherein: the set ofcontrol values describes the data-mining task; the set of data-miningtask parameters defines the data set and the data-mining task; and theset of computational resources is based at least in part on the dataset, the set of control values, and the set of data-mining taskparameters.

49. The computer system of item 48, wherein the fourth instructions toestimate the set of computational resources includes: (i) fifthinstructions executable by a device to cause the device to recall a setof prior computational resource estimates; and (ii) sixth instructionsexecutable by a device to cause the device to receive a set of priorcomputational resource costs; wherein: the set of prior computationalresource costs corresponds to the set of prior computational resourceestimates.

50. The computer system of item 49, wherein the fourth instructions toestimate the set of computational resources includes: (i) fifthinstructions executable by a device to cause the device to estimate aset of process flows for the data-mining task; and (ii) sixthinstructions executable by a device to cause the device to estimate aset of computational resources required for each process flow in the setof process flows; wherein: different flows in the set of process flowsrepresent different manners of completing the data-mining task.

What is claimed is:
 1. A method comprising: receiving a set of task parameters, the set of task parameters defining a target data set and a data-mining task; receiving a set of control values, the set of control values describing the data-mining task; receiving a set of data descriptors, the set of data descriptors describing the target data set; estimating a set of computational resources required to perform the data-mining task over a distributed computing system based at least in part on the set of task parameters, the set of control values, the set of data descriptors, and an availability of the distributed computing system; and estimating a cloud cost for the data mining task; wherein: at least the estimating step is performed by computer software running on computer hardware.
 2. The method of claim 1, further comprising: generating an estimated cost required to perform the data-mining task by deriving a set of cost descriptors, the set of cost descriptors including: a number of compute iterations required to perform the data-mining task, and a complexity level of each compute iteration.
 3. The method of claim 2, further comprising: reporting the estimated cost required to perform the data-mining task.
 4. The method of claim 1, further comprising: defining a type of the data-mining task; wherein: the type is one of a regression task, a classification task, and a clustering task.
 5. The method of claim 4, further comprising: receiving, for a clustering task type, a clustering function for evaluating a partitioning of data included in the target data set into a plurality of data clusters.
 6. The method of claim 1, further comprising: receiving a high-level task description of the data-mining task; deriving a set of derived parameters of the data-mining task based on the high-level task description using a task definition application; and estimating the computational resources for performing the data-mining task based on the set of derived parameters.
 7. A computer program product comprising a computer readable storage medium having stored thereon: first instructions executable by a device to cause the device to receive a set of task parameters, the set of task parameters defining a target data set and a data-mining task; second instructions executable by a device to cause the device to receive a set of control values, the set of control values describing the data-mining task; third instructions executable by a device to cause the device to receive a set of data descriptors, the set of data descriptors describing the target data set; fourth instructions executable by a device to cause the device to estimate a set of computational resources required to perform the data-mining task over a distributed computing system based at least in part on the set of task parameters, the set of control values, the set of data descriptors, and an availability of the distributed computing system; and fifth instructions executable by a device to cause the device to estimate a cloud cost for the data mining task.
 8. The computer program product of claim 7, further comprising: sixth instructions executable by a device to cause the device to generate an estimated cost required to perform the data-mining task by deriving a set of cost descriptors, the set of cost descriptors including: a number of compute iterations required to perform the data-mining task, and a complexity level of each compute iteration.
 9. The computer program product of claim 8, further comprising: seventh instructions executable by a device to cause the device to report the estimated cost required to perform the data-mining task.
 10. The computer program product of claim 7, further comprising: sixth instructions executable by a device to cause the device to define a type of the data-mining task; wherein: the type is one of a regression task, a classification task, and a clustering task.
 11. The computer program product of claim 10, further comprising: seventh instructions executable by a device to cause the device to receive, for a clustering task type, a clustering function for evaluating a partitioning of data included in the target data set into a plurality of data clusters.
 12. The computer program product of claim 7, further comprising: sixth instructions executable by a device to cause the device to receive a high-level task description of the data-mining task; seventh instructions executable by a device to cause the device to derive a set of derived parameters of the data-mining task based on the high-level task description using a task definition application; and eighth instructions executable by a device to cause the device to estimate the computational resources for performing the data-mining task based on the set of derived parameters.
 13. A computer system comprising: a processor set; and a computer readable storage medium; wherein: the processor set is structured, located, connected, and/or programmed to execute instructions stored on the computer readable storage medium; and the instructions include: first instructions executable by a device to cause the device to receive a set of task parameters, the set of task parameters defining a target data set and a data-mining task; second instructions executable by a device to cause the device to receive a set of control values, the set of control values describing the data-mining task; third instructions executable by a device to cause the device to receive a set of data descriptors, the set of data descriptors describing the target data set; fourth instructions executable by a device to cause the device to estimate a set of computational resources required to perform the data-mining task over a distributed computing system based at least in part on the set of task parameters, the set of control values, the set of data descriptors, and an availability of the distributed computing system; and fifth instructions executable by a device to cause the device to estimate a cloud cost for the data mining task.
 14. The computer system of claim 13, further comprising: sixth instructions executable by a device to cause the device to generate an estimated cost required to perform the data-mining task by deriving a set of cost descriptors, the set of cost descriptors including: a number of compute iterations required to perform the data-mining task, and a complexity level of each compute iteration.
 15. The computer system of claim 14, further comprising: seventh instructions executable by a device to cause the device to report the estimated cost required to perform the data-mining task.
 16. The computer system of claim 13, further comprising: sixth instructions executable by a device to cause the device to define a type of the data-mining task; wherein: the type is one of a regression task, a classification task, and a clustering task.
 17. The computer system of claim 16, further comprising: seventh instructions executable by a device to cause the device to receive, for a clustering task type, a clustering function for evaluating a partitioning of data included in the target data set into a plurality of data clusters.
 18. The computer system of claim 13, further comprising: sixth instructions executable by a device to cause the device to receive a high-level task description of the data-mining task; seventh instructions executable by a device to cause the device to derive a set of derived parameters of the data-mining task based on the high-level task description using a task definition application; and eighth instructions executable by a device to cause the device to estimate the computational resources for performing the data-mining task based on the set of derived parameters. 